1. Field of the Invention
The present invention relates to a system and method for planning and scheduling customer orders. More specifically, the present invention relates to a computationally efficient method and system for fulfillment of customer orders in a supply chain by planning and scheduling multiple customer orders, planning for use the various resources located in remote locations needed to fulfill such orders, and scheduling the used resources for replenishment at appropriate times so that the resources meet the needs of the orders.
2. Discussion of the Related Art
Today's business environment is more competitive than ever before. Increasingly demanding customers and the surge of strong global competition require reduced manufacturing cycle times and increased customization of products and services—all in addition to the traditional allocation challenges of constrained resources. Manufacturers are faced with volatile demand, reduced product life cycles, complex supply chains, and shrinking margins. To survive, they must have the ability to satisfy customer demand while maximizing profits, but traditional methods fall short of providing the modeling, optimization, and analysis tools required to manage these complex manufacturing environments. When the right materials are not available when and where they are needed, many companies experience manufacturing delays, expediting costs, higher material costs, and poor customer service.
The production planning tools that have been developed in an attempt to address these business needs generally require complex models and analysis of production environments and the supporting processes. The most comprehensive algorithm-based analysis methods require levels of computation that preclude modeling of an entire production process in a dynamic environment. Heuristic planning tools provide a less computationally intensive solution to plant optimization models; but many sacrifice the ability to model key features of complex plant operations such as use of tank-type resources, personnel variations, rescheduling after material delays, and T-plant type modeling. Thus, there remains a need in the art for a computationally efficient method and system for planning and scheduling multiple customer orders that provides advanced modeling of production processes.